Related Experiment Video
Updated: Jan 8, 2026
09:20
Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction
Published on: February 13, 2021
7.0K
Modeling Brain-Heart Interaction: A Review of Mechanistic Dynamical Models
IEEE Reviews in Biomedical Engineering
|December 23, 2025
Summary
Brain-heart interaction (BHI) is crucial for autonomic regulation and mental processes. This review focuses on mechanistic dynamical systems models to understand BHI, identify biomarkers, and improve clinical applications.
Area of Science:
- Neuroscience
- Physiology
- Cardiovascular Science
Background:
- Brain-heart interaction (BHI) is vital for autonomic regulation, cognition, and emotion.
- BHI dysfunction is linked to cardiovascular, neurological, and psychiatric disorders.
- Understanding BHI requires viewing the body as an interoceptive network.
Purpose of the Study:
- To review mechanistic, physiology-inspired dynamical systems models of BHI.
- To identify physiological subsystems, assumptions, strengths, and limitations of these models.
- To outline technical perspectives for inferring latent interoceptive quantities and integrating brain modeling.
Main Methods:
- Review of state-of-the-art dynamical models of BHI.
- Analysis of physiological subsystems, model assumptions, and limitations.
- Identification of technical requirements for advanced BHI modeling.
Main Results:
- Detailed account of current dynamical models for BHI.
- Delineation of methodological directions for BHI research.
- Highlighting applications for explanatory insight, prediction, and clinical targets.
Conclusions:
- Mechanistic dynamical modeling offers a powerful approach to understanding BHI.
- Future work should focus on inferring unobservable interoceptive signals and integrating neural mechanisms.
- These models can lead to improved diagnostics and treatments for various conditions.
Related Concept Videos
01:21Mechanistic Models: Overview of Compartment Models
329
Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
329
01:15Model Approaches for Pharmacokinetic Data: Physiological Models
234
Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
234
01:29Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
255
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
255
01:23Mechanistic Models: Compartment Models in Individual and Population Analysis
225
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
225
00:57Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models
312
Physiological pharmacokinetic models, often called flow-limited or perfusion models, typically assume a swift drug distribution between tissue and venous blood, creating a rapid drug equilibrium. This premise is based on the idea that drug diffusion is extremely fast, and the cell membrane presents no barrier to drug permeation. In this scenario, where no drug binding occurs, the drug concentration in the tissue equals that of the venous blood leaving the tissue. This greatly simplifies the...
312
01:09Clearance Models: Physiological Models
266
Drug clearance is a critical pharmacokinetic process involving the irreversible removal of drugs from the body through various organs over a specified time period. Physiological models are indispensable in determining organ-specific clearance, defined by the proportion of the drug eliminated per unit of time from the organ's blood volume.
The organ's clearance rate depends on the blood flow to the organ and the extraction ratio (E). The extraction ratio describes the organ's...
The organ's clearance rate depends on the blood flow to the organ and the extraction ratio (E). The extraction ratio describes the organ's...
266